Sisihan Piawai & Varians Kalkulator

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What Is a Population Measures Tool?

A standard deviation and variance calculator gives you both core measures of data spread in a single operation. Variance (σ² or s²) quantifies dispersion in squared units, while standard deviation (σ or s) converts it back to the original data units by taking the square root. Together, they provide a complete picture: variance is used in mathematical statistics (ANOVA, regression, portfolio theory), while standard deviation is the go-to for intuitive interpretation (the 68-95-99.7 rule, z-scores, confidence intervals). Enter your data once and get both measures for both population and sample formulas.

Formula

Variance and standard deviation are computed together — SD is simply the square root of variance.

Population Measures

σ² = Σ(xᵢ − μ)² / N → σ = √σ²
  • σ²Population variance
  • σPopulation standard deviation
  • μPopulation mean
  • NTotal values in the population

Sample Measures

s² = Σ(xᵢ − x̄)² / (n−1) → s = √s²
  • Sample variance
  • sSample standard deviation
  • Sample mean
  • n−1Degrees of freedom

How to Calculate Standard Deviation and Variance Together

  1. Compute the mean.x̄ = Σxᵢ / n.
  2. Subtract the mean from each value.Get deviations: (xᵢ − x̄).
  3. Square each deviation.Compute (xᵢ − x̄)².
  4. Sum all squared deviations.Σ(xᵢ − x̄)² — this is the Sum of Squares (SS).
  5. Divide by the appropriate denominator.Divide SS by N for population variance or by n − 1 for sample variance.
  6. Take the square root for standard deviation.SD = √variance. You now have both measures.

Practical Standard Deviation & Variance Examples

1Household Energy Bills ($)

Data:95, 110, 87, 102, 98, 115
Solution:Mean = 101.17. Squared deviations sum ≈ 557.33. Pop. variance = 92.89. Sample variance = 111.47.
Answer:σ ≈ 9.64, σ² = 92.89 | s ≈ 10.56, s² = 111.47

2Reaction Times (ms)

Data:245, 260, 238, 270, 255, 242, 265
Solution:Mean ≈ 253.57. Squared deviations sum ≈ 870.86. Pop. variance ≈ 124.41. Sample variance ≈ 145.14.
Answer:σ ≈ 11.15, σ² ≈ 124.41 | s ≈ 12.05, s² ≈ 145.14

3Rainfall (mm per month)

Data:50, 75, 30, 90, 60
Solution:Mean = 61. Deviations: −11, 14, −31, 29, −1. Squared: 121, 196, 961, 841, 1. Sum = 2120.
Answer:σ² = 424, σ ≈ 20.59 | s² = 530, s ≈ 23.02

4App Response Times (seconds)

Data:0.8, 1.2, 0.9, 1.5, 1.0, 0.7
Solution:Mean ≈ 1.017. Squared deviations sum ≈ 0.4583.
Answer:σ² ≈ 0.0764, σ ≈ 0.276 | s² ≈ 0.0917, s ≈ 0.303

5Exam Percentages

Data:88, 76, 95, 82, 91, 70, 85
Solution:Mean ≈ 83.86. Squared deviations sum ≈ 458.86.
Answer:σ² ≈ 65.55, σ ≈ 8.10 | s² ≈ 76.48, s ≈ 8.75

How to Use This SD & Variance Calculator

Get both measures of spread in one step:

  1. Enter your data values separated by commas, spaces, or line breaks.
  2. The calculator instantly returns population and sample versions of both variance and standard deviation.
  3. Expand the step-by-step section to see the full derivation from mean through to final results.
  4. Copy results for reports, academic submissions, or further statistical analysis.

Benefits of Calculating SD & Variance Together

  • One input, four key results — population and sample variance plus both standard deviations.
  • Saves time compared to running two separate calculations.
  • Step-by-step breakdown helps you understand how SD derives from variance.
  • Complementary statistics (mean, range, CV, sum of squares) included at no extra effort.
  • Perfect for assignments that require reporting both measures.

Where SD & Variance Are Used Together

Academic Statistics

Textbook problems and exams routinely ask for both measures.

ANOVA & Regression

Variance is used in model computations; SD is reported for interpretability.

Risk Management

Portfolio variance determines combined risk; SD communicates it in the asset's units.

Process Control

Control chart limits use SD, while capability indices use variance components.

Research Papers

Journals often require both measures in descriptive statistics tables.

Machine Learning

Feature scaling and normalization use SD; variance informs feature selection.

Choosing Between Standard Deviation and Variance

Both measure spread, but they serve different purposes. Knowing when to use each one avoids confusion and strengthens your analysis:

Use SD for communication

Standard deviation is in the same units as your data, making it immediately meaningful. Saying "the average is 50 ± 5 units" is intuitive; "the variance is 25 squared-units" is not.

Use variance for mathematical operations

Variances of independent variables add directly: Var(X + Y) = Var(X) + Var(Y). Standard deviations do not add this way, making variance essential in ANOVA, regression, and portfolio theory.

Use SD for the empirical rule

The 68-95-99.7 rule uses standard deviation to define data intervals. Approximately 68% of data falls within one SD of the mean.

Use variance for comparing model fits

R² (coefficient of determination) is based on variance decomposition — it tells you what proportion of total variance is explained by the model.

Report both when thoroughness matters

In scientific papers, dissertations, and quality reports, reporting both demonstrates statistical rigour and satisfies different readers' preferences.

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FAQ

Soalan Lazim — Sisihan Piawai & Varians Kalkulator

Semua yang anda perlu tahu tentang varians.

Apakah perbezaan antara sisihan piawai dan varians?

Ukuran Variance tersebar dalam unit kuasa dua — ini ialah purata sisihan kuasa dua daripada min. Sisihan piawai ialah punca kuasa dua varians, mengembalikan ukuran kepada unit asal. Contohnya, jika data dalam meter, varians adalah dalam m² manakala SD dalam meter. Kedua-duanya mengukur konsep yang sama (sebaran data), tetapi SD lebih mudah untuk ditafsirkan kerana ia berada pada skala yang sama dengan data.

Bagaimana untuk mengira sisihan piawai dan varians bersama-sama?

Proses ini dikongsi: 1. Cari min. 2. Tolak min daripada setiap nilai. 3. Kuadratkan setiap sisihan. 4. Jumlahkan sisihan kuasa dua. 5. Bahagikan dengan n (populasi) atau n−1 (sampel) — ini ialah varians. 6. Ambil punca kuasa dua — ini ialah sisihan piawai. Kalkulator kami mengira kedua-duanya secara serentak.

Bilakah saya harus melaporkan sisihan piawai vs varians?

Laporkan sisihan piawai apabila berkomunikasi dengan khalayak umum atau apabila anda memerlukan ukuran dalam unit asal (mis., "ketinggian berbeza sebanyak ±2.5 inci"). Laporkan varians dalam konteks teknikal di mana sifat matematiknya penting — ANOVA, analisis regresi, pengoptimuman portfolio dan menggabungkan varians pembolehubah bebas (varians ditambah; sisihan piawai tidak).

Apakah formula bagi sisihan piawai dan varians?

Varians populasi: σ² = Σ(xᵢ − μ)² / N. Varians sampel: s² = Σ(xᵢ − x̄)² / (n−1). SD Populasi: σ = √σ². Sampel SD: s = √s². Satu-satunya perbezaan antara versi populasi dan sampel ialah pembahagi: N vs n−1. Hubungan punca kuasa dua bermaksud SD = √variance dan varians = SD².

Bolehkah sisihan piawai lebih besar daripada varians?

Ya, apabila varians adalah antara 0 dan 1. Memandangkan SD = √variance, jika varians = 0.25 maka SD = 0.5, yang lebih besar. Apabila varians = 1, SD = 1 (ia adalah sama). Apabila varians > 1, SD < varians. Sebagai contoh, varians = 4 memberikan SD = 2. Ini adalah hubungan matematik semata-mata — punca kuasa dua nombor kurang daripada 1 adalah lebih besar daripada nombor itu sendiri.

Mengapa kita memerlukan sisihan piawai dan varians?

Masing-masing mempunyai kelebihan tersendiri. Variance: aditif untuk pembolehubah tidak bersandar (Var(X+Y) = Var(X) + Var(Y)), digunakan dalam ANOVA, regresi dan statistik matematik. Sisihan piawai: unit yang sama seperti data, boleh ditafsir dengan peraturan empirikal (peraturan 68-95-99.7), digunakan dalam skor z dan selang keyakinan. Bersama-sama mereka memberikan gambaran lengkap tentang penyebaran data untuk tafsiran praktikal dan analisis matematik.